Traditional video forgery detection methods [1]}, [2]}, [3]} include stitching detection, copy-paste detection, image restoration detection, etc.
For example, Subramanyam et al. [2]} finds that HoG features are robust against various signal processing manipulations.
Aloraini [3]} performs sequential analysis by modeling video sequences as stochastic processes. Changes in the parameters of these processes indicate a video forgery.
In recent years, most of the video forgery detection work focuses on the detection of forged faces [6]}, [7]}, [8]}, [9]}.
Li et al. [7]} considers both temporal and spatial information, and uses 3DCNN to discriminate forged videos.
Zhang et al. [8]} mined some traces in the frequency spectrum and detected the images generated by GAN in the frequency domain.
There are also several image manipulation detection schemes.
Wu et al. [12]} proposed an end-to-end deep neural network structure, ManTraNet, which first learns image manipulation traces through self-supervision, then extracts local anomalies through Z-score, and detects and locates multiple tampering by judging anomalies.
Dong et al. [13]} proposed MVSS-Net to augment the differences between the tampered and untampered regions at the boundary, and noise inconsistency and edge supervision are monitored to unveil image manipulation.
However, universal video tampering detection is still a hard issue. One reason is that the above methods either focus on a typical distribution of videos, such as facial clips, or cannot generalize well on compressed videos. Another reason is that video post-processing attacks represented by MPEG compression are complicated, and the ways of video tampering and post-processing are indefinite. Thus, it is extremely difficult to find a universal clue for all kinds of tampered videos.
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